Tim Good
Impact in
- Hardware and Architecture top 10%
- Physical Unclonable Functions (PUFs) and Hardware Security
Papers in
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- Cryptographic Implementations and Security 5
-
- Muscle activation and electromyography studies 6
- Co-authors
- Mohammed Benaissa (10 shared papers)Ben Heller (8 shared papers)Anthony T. Barker (6 shared papers)Mohammad R. Eissa (5 shared papers)Hui Zheng (3 shared papers)Glen Cooper (5 shared papers)Laurence Kenney (5 shared papers)David Howard (5 shared papers)
- Journals
- IEEE Journal of Biomedical and Health Informatics (2 papers)IET Information Security (1 paper)Gait & Posture (1 paper)BMC Neurology (1 paper)IEEE Transactions on Very Large Scale Integration (VLSI) Systems (1 paper)
- Partner nations
- United Kingdom
In The Last Decade
Tim Good
21 papers receiving 347 citations
Peers
Comparison fields: 5 of 66
- Hardware and Architecture 57
- Health Information Management 33
- Rehabilitation 46
- Computer Vision and Pattern Recognition 108
- Artificial Intelligence 168
Countries citing papers authored by Tim Good
This map shows the geographic impact of Tim Good's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tim Good with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Good more than expected).
Fields of papers citing papers by Tim Good
This network shows the impact of papers produced by Tim Good. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tim Good. The network helps show where Tim Good may publish in the future.
Co-authors
The 25 scholars most cited alongside Tim Good, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 75 | |
| 2 | 2009 | 47 | |
| 3 | 2012 | 43 | |
| 4 | 2007 | 42 | |
| 5 | 2020 | 36 | |
| 6 | 2011 | 22 | |
| 7 | 2014 | 18 | |
| 8 | 2014 | 17 | |
| 9 | A deep neural network application for improved prediction of HbA1c in type 1 diabetes | 2020 | 9 |
| 10 | 2020 | 7 | |
| 11 | 2022 | 7 | |
| 12 | 2016 | 7 | |
| 13 | 2012 | 5 | |
| 14 | 2015 | 5 | |
| 15 | 2009 | 5 | |
| 16 | 2010 | 4 | |
| 17 | 1985 | 4 | |
| 18 | 2006 | 4 | |
| 19 | 2023 | 3 | |
| 20 | 2022 | 2 |
About Tim Good
Tim Good is a scholar working on Artificial Intelligence, Biomedical Engineering, Cellular and Molecular Neuroscience, Endocrinology, Diabetes and Metabolism and Rehabilitation, having authored 23 papers that have together received 363 indexed citations. Recurring topics across this work include Neuroscience and Neural Engineering (6 papers), Muscle activation and electromyography studies (6 papers), Diabetes Management and Research (5 papers), Cryptographic Implementations and Security (5 papers), Stroke Rehabilitation and Recovery (4 papers), Physical Unclonable Functions (PUFs) and Hardware Security (4 papers), Cerebral Palsy and Movement Disorders (4 papers) and Chaos-based Image/Signal Encryption (4 papers). The work is most often cited by research in Hardware and Architecture (57 citations), Health Information Management (33 citations), Rehabilitation (46 citations), Computer Vision and Pattern Recognition (108 citations) and Artificial Intelligence (168 citations). Tim Good has collaborated with scholars based in United Kingdom. Frequent co-authors include Mohammed Benaissa, Ben Heller, Anthony T. Barker, Mohammad R. Eissa, Hui Zheng, Glen Cooper, Laurence Kenney, David Howard, Daisy Elliott and Timothy J. Healey. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, IET Information Security, Gait & Posture, BMC Neurology and IEEE Transactions on Very Large Scale Integration (VLSI) Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.